Disentangling Latent Space for Unsupervised Semantic Face Editing
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Editing facial images created by StyleGAN is a popular research topic with important applications. Through editing the latent vectors, it is possible to control the facial attributes such as smile, age, etc. However, facial attributes are entangled in the latent space and this makes it very difficult to independently control a specific attribute without affecting the others. The key to developing neat semantic control is to completely disentangle the latent space and perform image editing in an unsupervised manner. In this paper, we present a new technique termed Structure-Texture Independent Architecture with Weight Decomposition and Orthogonal Regularization (STIA-WO) to disentangle the latent space. The GAN model, applying STIA-WO, is referred to as STGAN-WO. STGAN-WO performs weight decomposition by utilizing the style vector to construct a fully controllable weight matrix for controlling the image synthesis, and utilizes orthogonal regularization to ensure each entry of the style vector only controls one factor of variation. To further disentangle the facial attributes, STGAN-WO introduces a structure-texture independent architecture which utilizes two independently and identically distributed (i.i.d.) latent vectors to control the synthesis of the texture and structure components in a disentangled way.Unsupervised semantic editing is achieved by moving the latent code in the coarse layers along its orthogonal directions to change texture related attributes or changing the latent code in the fine layers to manipulate structure related ones. We present experimental results which show that our new STGAN-WO can achieve better attribute editing than state of the art methods (The code is available at https://github.com/max-liu-112/STGAN-WO)
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